David John Lary

The University of Texas at Dallas | UTD · Physics

We introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectral imagery called Generative Simplex Mapping (GSM). The model represents endmember mixing using a latent space of points sampled within a (n−1)-simplex corresponding to n unique sources. Barycentric coordinates within this simplex are naturally interpre...

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost...

The goal of this study is to describe a design architecture for a self-powered IoT (Internet of Things) sensor network that is currently being deployed at various locations throughout the Dallas-Fort Worth metroplex to measure and report on Particulate Matter (PM) concentrations. This system leverages diverse low-cost PM sensors, enhanced by machin...

This study introduces a new approach to monitoring avian diversity and vocal behavior, as well as assessing their responses to environmental factors using IoT-based sensors. We utilized BirdNet, a deep learning model for identifying bird species by their vocalizations, to data from our MINTS-AI environmental sensors deployed across Dallas, Texas. T...

We introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectral imagery called Generative Simplex Mapping (GSM). The model represents endmember mixing using a latent space with points sampled within a (n−1)-simplex corresponding to the abundance of n unique sources. Points in this latent space are non-linearly ma...

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost...

This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical...

Unmanned aerial vehicles equipped with hyperspectral imagers have emerged as an essential technology for the characterization of inland water bodies. The high spectral and spatial resolutions of these systems enable the retrieval of a plethora of optically active water quality parameters via band ratio algorithms and machine learning methods. Howev...

This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical...

Modeling and forecasting the dynamics of complex systems, such as moderate pressure capacitively coupled plasma (CCP) systems, remains a challenge due to the interactions of physical and chemical processes across multiple scales. Historically, optimization for a given application would be accomplished via a design of experiment (DOE) study across t...

Introduction: Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM1 and PM2.5, carbon dioxide (CO2), nitrogen dioxide (NO2), and nitric oxide (NO) are exemplars of the wider human exposome. In this study, we adopted a unique approach by utilizing the responses of human autonomic sy...

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further developme...

In this study, we adopt a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Air pollution has numerous impacts on human health on a variety of time scales. This study uses biometric observations of the human autonomic response on the second timescale to investigate how the hum...

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the high-quality reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the furt...

The human body is an incredible and complex sensing system. Environmental factors trigger a wide range of automatic neurophysiological responses. Biometric sensors can capture these responses in real time, providing clues about the underlying biophysical mechanisms. In this prototype study, we demonstrate an experimental paradigm to holistically ca...

Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta. While these bands have been shown to be useful for characterizing various brain states, their utility as a one-size-fits-all analysis tool remains uncle...

The human body is an incredible and complex sensing system. Environmental factors trigger a wide range of automatic neurophysiological responses. Biometric sensors can capture these responses in real time, providing clues to the underlying biophysical mechanisms. Here we show biometric variables can be used to accurately estimate ultra-local partic...

The eyes serve as a window into underlying physical and cognitive processes. Although factors such as pupil size have been studied extensively, a less explored yet potentially informative aspect is blinking. Given its novelty, blink detection techniques are far less available compared to eye-tracking and pupil size estimation tools. In this work, w...

Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta.While these bands have been shown to be useful for characterizing various brain states, their utility as a one-size-fits-all analysis tool remains unclea...

The cover feature image shows a collage of a DNA strand, voltammetric curve, and machine learning code to represent the methods used in this work. Extraction of electron transfer rates from experimental voltammetry datasets tends to be cumbersome. This work utilizes machine learning as an artificial intelligence method to rapidly acquire rates from...

Black pod rot, caused by Phytophthora palmivora, is a devastating disease of Theobroma cacao L. (cacao) leading to huge losses for farmers and limiting chocolate industry supplies. To understand resistance responses of cacao leaves to P. palmivora, Stage 2 leaves of genotypes Imperial College Selection 1 (ICS1), Colección Castro Naranjal 51 (CCN51)...

PM2.5, a type of fine particulate with a diameter equal to or less than 2.5 micrometers, has been identified as a major source of air pollution, and is associated with many health issues. Research on utilizing various data sources, such as remote sensing and in situ sensors, for PM2.5 concentrations modeling remains a hot topic. In this study, the...

Electrochemistry of surface‐bound molecules is of high importance for numerous electronic and sensor applications. Extracting the electron transfer rate is beneficial for understanding surface‐bound processes, but it requires experimental or computational rigor. We evaluate methods to determine electron transfer rates from large voltammetry sets fr...

In this study, we present a nationwide machine learning model for hourly PM2.5 estimation for the continental United States (US) using high temporal resolution Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) data, meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF)...

Sunlight incident on the Earth’s atmosphere is essential for life, and it is the driving force of a host of photo-chemical and environmental processes, such as the radiative heating of the atmosphere. We report the description and application of a physical methodology relative to how an ensemble of very low-cost sensors (with a total cost of <$20,...

Remote sensing imagery, such as that provided by the United States Geological Survey (USGS) Landsat satellites, has been widely used to study environmental protection, hazard analysis, and urban planning for decades. Clouds are a constant challenge for such imagery and, if not handled correctly, can cause a variety of issues for a wide range of rem...

In this research, we present data‐driven forecasting of ionospheric total electron content (TEC) using the Long‐Short Term Memory (LSTM) deep recurrent neural network method. The random forest machine learning method was used to perform a regression analysis and estimate the variable importance of the input parameters. The input data are obtained f...

By definition, human health and well-being are due to a complex interaction between a myriad of factors, a set of interactions for which we do not have a complete theoretical model, but often have an array of relevant data describing many aspects of this complex system. Machine learning is ideally suited for such situations. Over the last two decad...

Airborne particulates are of particular significance for their human health impacts and their roles in both atmospheric radiative transfer and atmospheric chemistry. Observations of airborne particulates are typically made by environment agencies using rather expensive instruments. Due to the expense of the instruments usually used by environment a...

This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous teams, and it is relevant to satellite calibration/validation and the creation of new remote sensing data products...

This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous teams, and is relevant to satellite calibration/validation and the creation of new remote sensing data products. A...

Machine learning has found many applications in Earth Science. These applications range from retrieval algorithms, from code acceleration to calibration of low cost sensors, from classification of dust sources, to rock type classification. As a broad sub-field of artificial intelligence, machine learning is concerned with algorithms and techniques...

The “science-softCon UV/Vis⁺ Photochemistry Database” (www.photochemistry.org) is a large and comprehensive collection of EUV-VUV-UV-Vis-NIR spectral data and other photochemical information assembled from published peer-reviewed papers. The database contains photochemical data including absorption, fluorescence, photoelectron, and circular and lin...

Airborne particulates are of particular significance for their human health impacts and their roles in both atmospheric radiative transfer and atmospheric chemistry. Observations of airborne particulates are typically made by environmental agencies using rather expensive instruments. Due to the expense of the instruments usually used by environment...

The human body exhibits a variety of autonomic responses. For example, changing light intensity provokes a change in the pupil dilation. In the past, formulae for pupil size based on luminance have been derived using traditional empirical approaches. In this paper, we present a different approach to a similar task by using machine learning to ex- a...

Dear Colleagues, we have a limited number of free copies of the 12th Edition (2019) “UV/Vis+ Spectra Data Base(UV/Vis+ Photochemistry Database)” CD-ROM available. Please note that these free copies will only distributed for academic, non-commercial utilization (Universities, governmental organizations). Please contact helpdesk@science-softcon.de ....

CMOS IC technology has become an affordable means for implementing capable systems operating at 300 GHz and above. CMOS circuits have been used to generate a signal up to 1.3 THz to detect both amplitude and phase of signals up to 1.2 THz, and to detect a signal amplitude up to ~10 THz. Additionally, a transmitter and a receiver operating up to ~30...

Approximately 50 million Americans have allergic diseases. Airborne plant pollen is a significant trigger for several of these allergic diseases. Ambrosia (ragweed) is known for its abundant production of pollen and its potent allergic effect in North America. Hence, estimating and predicting the daily atmospheric concentration of pollen (ragweed p...

In this study, we found that machine learning was able to effectively estimate student learning outcomes geo-spatially across all the campuses in a large, urban, independent school district. The machine learning showed that key factors in estimating the student learning outcomes included the number of days students were absent from school. In turn,...

In order to examine associations between asthma morbidity and local ambient air pollution in an area with relatively low levels of pollution, we conducted a time-series analysis of asthma hospital admissions and fine particulate matter pollution (PM2.5) in and around Jackson, MS, for the period 2003 to 2011. Daily patient-level records were obtaine...

PM2.5 air pollution is a significant issue for human health all over the world, especially in East Asia. A large number of ground-based measurement sites have been established over the last decade to monitor real-time PM2.5 concentration. However, even this enhanced observational network leaves many gaps in characterizing the PM2.5 spatial distribu...

For the period of the Barnett Coordinated Campaign, October 16–31, 2013, hourly concentrations for 46 volatile organic compounds (VOCs) were recorded at 14 air monitoring stations within the Barnett Shale of North Texas. These measurements are used to identify and analyze multi-species hydrocarbon signatures on a regional scale through the novel co...

Millions of people have an allergic reaction to pollen. The impact of pollen allergies is on the rise due to increased pollen levels caused by global warming and the spread of highly invasive weeds. The production, release, and dispersal of pollen depend on the ambient weather conditions. The temperature, rainfall, humidity, cloud cover, and wind a...

Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a method for the robust estimation of the concentration of airborne Ambrosia pollen using a suite of mac...

A Global Ocean Carbon Algorithm Database (GOCAD) has been developed from over 500 oceanographic field campaigns conducted worldwide over the past 30 years including in situ reflectances and coincident satellite imagery, multi- and hyperspectral Chromophoric Dissolved Organic Matter (CDOM) absorption coefficients from 245–715 nm, CDOM spectral slope...

In response to the challenge of military traumatic brain injury and posttraumatic stress disorder, the US military developed a wide range of holistic care modalities at the new Walter Reed National Military Medical Center, Bethesda, MD, from 2001 to 2017, guided by civilian expert consultation via the Epidaurus Project. These projects spanned a ran...

Machine learning has found many applications in remote sensing. These applications range from retrieval algorithms to bias correction, from code acceleration to detection of disease in crops, from classification of pelagic habitats to rock type classification. As a broad subfield of artificial intelligence, machine learning is concerned with algori...

CMOS (Complementary Metal Oxide Silicon) integrated circuits (IC’s) technology is emerging as a means for realization of capable and affordable systems that operate at 300GHz and higher. Despite the fact that the unity maximum available gain frequency, f max of NMOS transistors has peaked at ~320GHz somewhere between 65 and 32-nm technology nodes,...

This article describes an example of using machine learning to estimate the abundance of airborne Ambrosia pollen for Tulsa, OK. Twenty-seven years of historical pollen observations were used. These pollen observations were combined with machine learning and a very complete meteorological and land surface context of 85 variables to estimate the dai...

The abundance of airborne particulate matter with an aerodynamic equivalent diameter of 2.5μm or less (PM2.5) is a significant environmental and health issue. Many tools have been used to examine the relationship between PM2.5 abundance and meteorological variables, but some of the relationships are nonlinear, non-Gaussian, and even unknown. Machin...

Phytophthora megakarya (Pmeg) and Phytophthora palmivora (Ppal) cause black pod rot of Theobroma cacao L. (cacao). Of these two clade 4 species, Pmeg is more virulent and is displacing Ppal in many cacao production areas in Africa. Symptoms and species specific sporangia production were compared when the two species were co-inoculated onto pod piec...

Phytophthora megakarya (Pmeg) and Phytophthora palmivora (Ppal) are closely related species causing cacao black pod rot. Although Ppal is a cosmopolitan pathogen, cacao is the only known host of economic importance for Pmeg. Pmeg is more virulent on cacao than Ppal. We sequenced and compared the Pmeg and Ppal genomes and identified virulence-relate...

Spectral information (gas, liquid and solid phase from EUV-VUV-UV-Vis-NIR) and related data (e.g. information concerning publications on quantum yield studies or photolysis studies) from published papers. The 11th edition of the science-softCon "UV/Vis+ Spectra Data Base" contains about 10500 spectra/data sheets (ascii-format) as well as about 350...

Recent advances of CMOS technology and circuits have made it an alternative for realizing capable and affordable THz systems. With process and circuit optimization, it should be possible to generate useful power and coherently detect signals at frequencies beyond 1THz, and incoherently detect signals at 40THz in CMOS.

We describe the Ignite Distributed Collaborative Visualization System (IDCVS), a system which permits real-time interaction and visual collaboration around large data sets on thin devices for users distributed about the wide area. The IDCVS provides seamless interaction and immediate updates even under heavy load and when users are widely separated...

There is an increasing awareness of the health impacts of particulate matter and a growing need to quantify the spatial and temporal variations of the global abundance of ground level airborne particulate matter (PM2.5). In March 2014, the World Health Organization (WHO) released a report that in 2012 alone, a staggering 7 million people died as a...